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# ML Data Scientist, Apple Pay Analytics

- **Company:** Apple Inc.
- **Location:** United States
- **Experience:** Expert
- **Contract:** Permanent contract
- **Skills:** Data Analysis, Information Engineering, Data Visualization, Python (Programming Language), Machine Learning, SQL Databases, Tableau (Software)
- **Published:** September 30, 2026
- **Apply:** https://www.dice.com/job-detail/5f686e99-dd06-431b-ae56-cd6decd6008d

## About the Role

5+ years of demonstrated experience in a Machine Learning Engineer role.

Strong business acumen and the ability to think strategically and operationally.

Experience with time series forecasting methods and ML techniques for business analysis (regression, classification, clustering) to solve strategic business problems.

Be a self-starter, driven, accountable and a high-energy team player.

Proven experience being a thought partner to cross-functional business teams offering actionable data insights and recommendations.

Demonstrated ability to influence without authority.

Excellent communication skills-able to distill complex analysis into simple, compelling narratives. Background in consulting or customer-facing, fast-paced analytical environment.

Expertise with SQL, R or Python and data visualization tools such as Tableau for full-stack data analysis, insight synthesis and presentation.

Knowledge of and experience in leveraging Applied Statistical/ML techniques.

Bachelor's degree in Economics, Finance, Statistics, Mathematics, Econometrics, or Applied Social Sciences.

Preferred Qualifications

Demonstrated experience in an Analytics and Strategy role, preferably in the payments/fintech domain

Ability to understand ambiguous and complex problems and design and execute analytical approaches and turn analysis into clear and concise takeaways that drive action

Track record of leading organization-wide strategic initiatives or transformative analytics projects

Master's degree in Economics, Finance, Statistics, Mathematics, Econometrics, or Applied Social Sciences.

## Description

Your recommendations will directly shape decisions at the highest levels, while you partner with the extended data sciences & data engineering to ensure reliable, accessible data. We work fast and iteratively, value open feedback and debate, and balance teamwork with independent decision-making and smart risk-taking.


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